Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles

• Published in: 2019 IEEE Global Communications Conference (GLOBECOM) pp. 1 - 6
• Main Authors: Yang, Li, Moubayed, Abdallah, Hamieh, Ismail, Shami, Abdallah
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2019
• Subjects: Complexity theory; Computational modeling; Decision trees; Feature extraction; Intrusion detection; Radio frequency; Vegetation
• ISSN: 2576-6813
• DOI: 10.1109/GLOBECOM38437.2019.9013892

The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.